Abstract
We present efficient algorithms to compute simple and complex map algebra operations over raster data stored in main memory, using the k2-acc compact data structure. Raster data correspond to numerical data that represent attributes of spatial objects, such as temperature or elevation measures. Compact data structures allow efficient data storage in main memory and query them in their compressed form. A k2-acc is a set of k2-trees, one for every distinct numeric value in the raster matrix. We demonstrate that map algebra operations can be computed efficiently using this compact data structure. In fact, some map algebra operations perform over five orders of magnitude faster compared with algorithms working over uncompressed datasets.
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Notes
A data warehouse is a data repository that stores consolidate data of a business to improve the decisions-making process.
If the matrix is n × m with n and m any integer, we conceptually extend the input matrix to the right and to the bottom with 0s, that is, we round n and m up to the next power of k of their maximum value.
The repositories are, respectively, available at http://gitlab.face.ubiobio.cl:8081/18978826/algebra-de-mapas-sobre-k2-acc/tree/master/K2TreeAccum and https://gitlab.lbd.org.es/fsilva/k2-raster
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Acknowledgements
Mónica Caniupán is partially funded by projects DIUBB [181315 3/R] and [2030228 If/R]. Rodrigo Torres-Avilés is partially funded by project DIUBB [181315 3/R]. The authors are part of the Algorithms and Databases Research Group [195119 GI/VC]. We would like to thank the useful conversations with some of the authors of the k2-raster.
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Caniupán, M., Torres-Avilés, R., Gutiérrez-Bunster, T. et al. Efficient computation of map algebra over raster data stored in the k2-acc compact data structure. Geoinformatica 26, 95–123 (2022). https://doi.org/10.1007/s10707-021-00445-y
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DOI: https://doi.org/10.1007/s10707-021-00445-y